#n_components wei du
#svd_solver method
import  matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.datasets import load_iris

data = load_iris()

y = data.target
x = data.data

pca = PCA(n_components=2)
reduced_X = pca.fit_transform(x)
#
# reduced_X = x
red_x, red_y = [],[]
blue_x,blue_y = [],[]
green_x,green_y = [],[]
for i in range(len(reduced_X)):
    if y[i] == 0:
        red_x.append(reduced_X[i][0])
        red_y.append(reduced_X[i][1])
    elif y[i] ==1 :
        blue_x.append(reduced_X[i][0])
        blue_y.append(reduced_X[i][1])
    else:
        green_x.append(reduced_X[i][0])
        green_y.append(reduced_X[i][1])

plt.scatter(red_x,red_y,c='r',marker='x')
plt.scatter(blue_x,blue_y,c='b',marker='D')
plt.scatter(green_x,green_y,c='g',marker='.')
plt.show()